Master Machine Learning Algorithms

Master Machine Learning Algorithms

Finally Pull Back The Curtain And See How They Work With
Clear Descriptions, Step-By-Step Tutorials and Working Examples in Spreadsheets

$37 USD

You must understand the algorithms to get good (and be recognized as being good) at machine learning.

In this mega Ebook is written in the friendly Machine Learning Mastery style that you’re used to, finally cut through the math and learn exactly how machine learning algorithms work, then implement them from scratch, step-by-step.

No Fancy Math and Nowhere for Details to Hide

Jason’s book is the best that exists to turn reasonably intelligent individuals with basic programming skills (any language) into sharp machine learning developers.

Howard SchneiderPhysician

You Learn Best By Implementing Algorithms From Scratch…But You Need Help With The First Step: The Math

Developers Learn Fast By Trying Things Out…

I’m a developer and I feel like I don’t really understand something until I can implement it from scratch. I need to understand each piece of it in order to understand the whole. The same thing applies to machine learning algorithms.

If you are anything like me, you will not feel comfortable about machine learning algorithms until you can implement them from scratch, step-by-step.

The Math Can Really Slow You Down (…and Sap Your Motivation)

The problem is, machine learning algorithms are not like other algorithms you may have implemented like sorting. They are always described using complex mathematics with a mixture of probability, statistics and linear algebra.

You need to be able to get past the mathematical descriptions in order to implement the algorithms from scratch, but you don’t have the time to spend 3 years studying mathematics to get there.

You Really Need Clear Worked Examples (…step-by-step with real numbers)

Machine learning algorithms would be much easier to understand if someone simplified the math and gave clear worked examples showing how real numbers get plugged into the equations and what numbers to expect as outputs. With clear inputs and outputs we as developers can reproduce and understand the math.

Even better would be to have worked examples that actually perform all of the calculation required to learn a model from a small sample dataset, and all of the calculations required to make predictions from the learned model.

Master Machine Learning Algorithms is for Developers….with NO Background in Math …and LOTS of Interest in Machine Learning

Introducing the “Master Machine Learning Algorithms” Ebook. This Ebook was carefully designed to provide a gentle introduction of the procedures to learn models from data and make predictions from data 10 popular and useful supervised machine learning algorithms used for predictive modeling.

Each algorithm includes a one or more step-by-step tutorials explaining exactly how to plug in numbers into each equation and what numbers to expect as output. These tutorials will guide you step-by-step through the processes for creating models from training data and making predictions.

More than that, each tutorial is designed to be completed in a spreadsheet. Spreadsheets are the simplest way to automate calculations and anyone can use a spreadsheet, from beginners, to professional developers to hard core programmers.

If you can understand how a machine learning algorithm works in a spreadsheet then you really know how it works. You can then implement it in any programming language you wish or use your newfound knowledge and understanding to achieve better performance from the algorithms in practice.

Everything You Need To Know About 10 Top Machine Learning Algorithms

This ebook was written around two themes designed to help you understand machine learning algorithms as quickly as possible.

These two parts are Algorithm Descriptions and Algorithm Tutorials:

Algorithm Descriptions: Discover exactly what each algorithm is and generally how it works from a high-level.Algorithm Tutorials: Climb inside each machine learning algorithm and work through a case study to see how it learns and makes predictions.

1. Algorithm Descriptions

Here is an overview of the linear, nonlinear and ensemble algorithm descriptions:

Algorithm 1: Gradient Descent.

Algorithm 2: Linear Regression.

Algorithm 3: Logistic Regression.

Algorithm 4: Linear Discriminant Analysis.

Algorithm 5: Classification and Regression Trees.

Algorithm 6: Naive Bayes.

Algorithm 7: K-Nearest Neighbors.

Algorithm 8: Learning Vector Quantization.

Algorithm 9: Support Vector Machines.

Algorithm 10: Bagged Decision Trees and Random Forest.

Algorithm 11: Boosting and AdaBoost.

2. Algorithm Tutorials

Here is an overview of the step-by-step algorithm tutorials:

Tutorial 1: Simple Linear Regression using Statistics.

Tutorial 2: Simple Linear Regression with Gradient Descent.

Tutorial 3: Logistic Regression with Gradient Descent.

Tutorial 4: Linear Discriminant Analysis using Statistics.

Tutorial 5: Classification and Regression Trees with Gini.

Tutorial 6: Naive Bayes for Categorical Data.

Tutorial 7: Gaussian Naive Bayes for Real-Valued Data.

Tutorial 8: K-Nearest Neighbors for Classification.

Tutorial 9: Learning Vector Quantization for Classification.

Tutorial 10: Support Vector Machines with Gradient Descent.

Tutorial 11: Bagged Classification and Regression Trees.

Tutorial 12: AdaBoost for Classification.

Each tutorial was designed to be completed in about 30 minutes by the average developer.

Master Machine Learning Algorithms Table of Contents

Here’s Everything You’ll Get In…Master Machine Learning Algorithms

Algorithm Tutorials and Spreadsheets

A digital download that contains everything you need, including:

Clear algorithm descriptions that help you to understand the principles that underlie each technique.

The step-by-step algorithm tutorials show you exactly how each model learns.

Spreadsheets showing all the examples and calculations from the book, giving you working models to use, learn from and extend.

Real worked examples so that you can see exactly the numbers in and the numbers out, there’s nowhere for the details to hide.

Digital Ebook in PDF format so that you can have the book open side-by-side with the spreadsheets and see exactly how each model works.

The grounding needed to understand algorithm behavior so that you can choose which algorithm to use and diagnose issues.

Important foundation principles for all machine learning algorithms, including:

The statistical and computer science terms used to describe data, and what they all mean (with pictures).

The fundamental problem that all machine learning algorithms solve and why it’s important.

The breakdown of algorithms as parametric and nonparametric and when to use each.

The important distinction between supervised and unsupervised techniques, and why you should just focus on one.

The modeling error introduced by bias and variance and how to balance them.

The poor algorithm performance that caused by overfitting and underfitting, and the techniques to identify and mitigate both.

Resources you need to go deeper, when you need to, including:

Top machine learning textbooks to deepen your foundation of machine learning algorithms, if you crave more.

The best forums and question-and-answer websites, places where you can ask your challenging questions and actually get a response.

Linear and Nonlinear Algorithms

Get the most from linear algorithms, the starting point for most projects, including:

The important functions in Excel, so that there is nothing holding you back from understanding how machine learning algorithms work.

Tips to get the most out of gradient descent, the core of many algorithms.

A clever shortcut you can use to greatly simplify linear regression.

The application of gradient descent to linear and logistic regression for fast and robust learning, and the specific numbers calculated at each step in the process.

The linear algorithm to use for classifications with more than two classes when logistic regression just won’t do.

Get better performance with more advanced nonlinear algorithms including:

The procedure for building up a decision tree, and carefully explained cost function you need to know to make it work.

The Bayes Theorem and the clever simplification that lets you harness the power of probability for predictive modeling.

The simple little technique that lets you use Bayesian probability on your real-valued data.

The simple but powerful nearest neighbor method and the problem that can trip you up when you have a lot of data features.

A clever simplification of nearest neighbors that uses learning rather than “a big dumb database of observations”.

The simple principle behind the wildly used Support Vector Machines method and how it translates into a real predictive modeling algorithm.

Combine the predictions from many models with ensemble algorithms, including:

The interesting bootstrap method for estimating quantities and how it can be easily applied as the basis for the Random Forest algorithm, perhaps the most popular machine learning algorithm used today.

The idea of creating models to fix the mistakes of other models and how this can be scaled up to achieve impressive results.

What More Do You Need?

Take a Sneak Peek Inside The Ebook

Below are some snapshots of select pages from the Ebook. Click to enlarge.

Each machine learning algorithm tutorial presented in the book is standalone, meaning that you can dive in anywhere and pickup where you left off anytime.

You get 16 Excel spreadsheets, one for each machine learning algorithm tutorial in the book.

This means that you can follow along and compare your answers to a known working implementation of each algorithm in the provided spreadsheets.

This helps a lot to speed up you progress when working through the detail of an algorithm.

Master Machine Learning Algorithms Spreadsheets

About The Author

Hi, I'm Jason Brownlee. I run this site and I wrote and published this book.

I live in Australia with my wife and sons. I love to read books, write tutorials, and develop systems.

I have a computer science and software engineering background as well as Masters and PhD degrees in Artificial Intelligence with a focus on stochastic optimization.

I've written books on algorithms, won and ranked well in competitions, consulted for startups, and spent years in industry. (Yes, I have spend a long time building and maintaining REAL operational systems!)

I get a lot of satisfaction helping developers get started and get really good at applied machine learning.

I teach an unconventional top-down and results-first approach to machine learning where we start by working through tutorials and problems, then later wade into theory as we need it.

I'm here to help if you ever have any questions. I want you to be awesome at machine learning.

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Check Out What Customers Are Saying:

Machine Learning Mastery is a perfect blend of math, statistics, and computer science contexts packaged in a practical approach to learning the key points of Machine Learning. This is a great book for more than curious Engineers and Manager types who want a clear, rich, and fact-filled summary of the field of Machine Learning.

Doug SchmidtChief Enterprise Architect at Pearson PLC

This book is exactly what it claims to be. I’ve tried many courses, books and tutorials on machine learning before and the math notation has always been a barrier. This book doesn’t avoid the notation, but presents it in a way that programmers will not only understand, bit also realise they’ve been speaking the same language all along.

Machine learning is difficult to comprehend, but this book reduces the challenge down to simple study and practice that any programmer should be able to handle without having to also learn or re-learn mathmatics.

Tom EldersTechnical Director at Applied Works

Nice to have crisp content of all the algorithms nicely explained in the guide. Approach towards application is the best part of it. I wish I’d got this guide earlier & have a head on start with ML algorithms.

Thanks Jason for expediting my journey towards ML !!

Tushar SaxenaBusiness Analyst @ Flipkart

This is a well written and well thought through book. There is enough information to write machine learning algorithms in any programming language from scratch.

I found this to be a quick read and greatly enhanced my understanding of machine learning algorithms.

Robert ChumleySr. Technical Consultant at Cetrest Corporation

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The books are full of tutorials that must be completed on the computer.

The books assume that you are working through the tutorials, not reading passively.

The books are intended to be read on the computer screen, next to a code editor.

The books are playbooks, they are not intended to be used as references texts and sit the shelf.

The books are updated frequently, to keep pace with changes to the field and APIs.

I hope that explains my rationale.

If you really do want a hard copy, you can purchase the book or bundle and create a printed version for your own personal use. There is no digital rights management (DRM) on the PDF files to prevent you from printing them.

I release new books every few months and develop a new super bundle at those times.

All existing customers will get early access to new books at a discount price.

Note, that you do get free updates to all of the books in your super bundle. This includes bug fixes, changes to APIs and even new chapters sometimes. I send out an email to customers for major book updates or you can contact me any time and ask for the latest version of a book.

The book “Master Machine Learning Algorithms” is for programmers and non-programmers alike. It teaches you how 10 top machine learning algorithms work, with worked examples in arithmetic, and spreadsheets, not code. The focus is on an understanding on how each model learns and makes predictions.

The book “Machine Learning Algorithms From Scratch” is for programmers that learn by writing code to understand. It provides step-by-step tutorials on how to implement top algorithms as well as how to load data, evaluate models and more. It has less on how the algorithms work, instead focusing exclusively on how to implement each in code.

The book “Deep Learning With Python” could be a prerequisite to”Long Short-Term Memory Networks with Python“. It teaches you how to get started with Keras and how to develop your first MLP, CNN and LSTM.

The book “Long Short-Term Memory Networks with Python” goes deep on LSTMs and teaches you how to prepare data, how to develop a suite of different LSTM architectures, parameter tuning, updating models and more.

The book “Deep Learning for Time Series Forecasting” focuses on how to use a suite of different deep learning models (MLPs, CNNs, LSTMs, and hybrids) to address a suite of different time series forecasting problems (univariate, multivariate, multistep and combinations).

The LSTM book teaches LSTMs only and does not focus on time series. The Deep Learning for Time Series book focuses on time series and teaches how to use many different models including LSTMs.

I do test my tutorials and projects on the blog first. It’s like the early access to ideas, and many of them do not make it to my training.

Much of the material in the books appeared in some form on my blog first and is later refined, improved and repackaged into a chapter format. I find this helps greatly with quality and bug fixing.

The books provide a more convenient packaging of the material, including source code, datasets and PDF format. They also include updates for new APIs, new chapters, bug and typo fixing, and direct access to me for all the support and help I can provide.

I believe my books offer thousands of dollars of education for tens of dollars each.

They are months if not years of experience distilled into a few hundred pages of carefully crafted and well-tested tutorials.

I think they are a bargain for professional developers looking to rapidly build skills in applied machine learning or use machine learning on a project.

Also, what are skills in machine learning worth to you? to your next project? and you’re current or next employer?

Nevertheless, the price of my books may appear expensive if you are a student or if you are not used to the high salaries for developers in North America, Australia, UK and similar parts of the world. For that, I am sorry.

It is a matching problem between an organization looking for someone to fill a role and you with your skills and background.

That being said, there are companies that are more interested in the value that you can provide to the business than the degrees that you have. Often, these are smaller companies and start-ups.

You can focus on providing value with machine learning by learning and getting very good at working through predictive modeling problems end-to-end. You can show this skill by developing a machine learning portfolio of completed projects.

My books are specifically designed to help you toward these ends. They teach you exactly how to use open source tools and libraries to get results in a predictive modeling project.